Recent advances have fostered a learning-based depth fusion fashion. [1]}, [2]}, [3]} seek to find a compact and optimisable feature for estimated monocular depth, where the entire scene is represented by a set of keyframes with low-dimensional depth codes. Further extensions utilize neural networks to learn aggregation of learned image/depth features in a latent space [4]}, [5]}, [6]}, [7]}, [8]}, [9]} as the global scene representation. RoutedFusion [10]} follows the conventional TSDF fusion pipeline and learns how volumetric TSDF is updated. However, all previous approaches view depth fusion as a deterministic learnable operation. We, on the other hand, address the problem of knowledge fusion as a continual learning (training) procedure, where the neural network serves as a self-improving scene representation with parameters updated continually.
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